The SoftVue ultrasound tomography (UST) system provides a solution for imaging the whole breast by combining volumetric imaging capability with cost-efficacy, absence of ionizing radiation and operator independence. Our long term goal is to develop imaging system that rivals the imaging performance of MRI in order to provide a low cost alternative to MRI and an expanded role in breast screening. In Phase I we demonstrated, based on numerical models, that waveform tomography can achieve MRI-like images in terms of resolution and can be implemented on clinically relevant time scales. In subsequent preliminary studies we have applied waveform tomography to in vivo data. The objective of this Phase II study is to refine waveform tomography and integrate it into a clinical software package that would be ready for productization, in support of our commercialization plan. Our central hypothesis is that the integrated waveform tomography will be able to produce in vivo breast images with MRI-like quality on clinically relevant time scales. The hypothesis will be tested via a study of 30 patients who have been identified by standard of care to have suspicious lesions and also have MR images available for comparison. The expected outcome is a clinical validation of the proposed technique, which, in turn, will support SoftVue's role as a diagnostic modality for breast imaging, in the short term, and as a screening modality, in the long term. The rationale for this study is that it will advance SoftVue to achieve our goal of eliminating the dilemma between imaging cost and imaging performance, and support commercialization of the SoftVue system. Our proposed study will be carried out via the following specific aims. Aim 1: Assess the performance of the proposed technique on data acquired with the SoftVue system. Aim 2: Implement design of the new SoftVue blade server from the Phase I study to reflect state-of-the-art architecture for both CPUs and GPUs and thereby maximize the computational capacity. Aim 3: Implement and test the proposed algorithm on the new SoftVue blade server architecture using in vivo data and assess the computational speed for clinical applications. Aim 4: Package the algorithm with the SoftVue product and support SoftVue's FDA applications.